Top

Published in:

Open Access 01-12-2021 | Research article

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

Authors: Eric L. Harshfield, Eric B. Fauman, David Stacey, Dirk S. Paul, Daniel Ziemek, Rachel M. Y. Ong, John Danesh, Adam S. Butterworth, Asif Rasheed, Taniya Sattar, Zameer-ul-Asar, Imran Saleem, Zoubia Hina, Unzila Ishtiaq, Nadeem Qamar, Nadeem Hayat Mallick, Zia Yaqub, Tahir Saghir, Syed Nadeem Hasan Rizvi, Anis Memon, Mohammad Ishaq, Syed Zahed Rasheed, Fazal-ur-Rehman Memon, Anjum Jalal, Shahid Abbas, Philippe Frossard, Danish Saleheen, Angela M. Wood, Julian L. Griffin, Albert Koulman

Published in: BMC Medicine | Issue 1/2021

Abstract

Background

Genetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies.

Methods

We characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci.

Results

We identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci.

Conclusions

Our findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

Available only for authorised users

Griffin JL, Atherton H, Shockcor J, Atzori L. Metabolomics as a tool for cardiac research. Nat Rev Cardiol. 2011;8(11):630–43. https://doi.org/10.1038/nrcardio.2011.138.CrossRefPubMed

Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100(4):635–49. https://doi.org/10.1016/J.AJHG.2017.03.004.CrossRefPubMedPubMedCentral

Jha P, McDevitt MT, Halilbasic E, Williams EG, Quiros PM, Gariani K, et al. Genetic regulation of plasma lipid species and their association with metabolic phenotypes. Cell Syst. 2018;6(6):709–21. https://doi.org/10.1016/j.cels.2018.05.009.CrossRefPubMedPubMedCentral

Jha P, McDevitt MT, Gupta R, Quiros PM, Williams EG, Gariani K, et al. Systems analyses reveal physiological roles and genetic regulators of liver lipid species. Cell Syst. 2018;6(6):722–33. https://doi.org/10.1016/J.CELS.2018.05.016.CrossRefPubMedPubMedCentral

Saleheen D, Zaidi M, Rasheed A, Ahmad U, Hakeem A, Murtaza M, et al. The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia. Eur J Epidemiol. 2009;24(6):329–38. https://doi.org/10.1007/s10654-009-9334-y.CrossRefPubMedPubMedCentral

Howson JMM, Zhao W, Barnes DR, Ho WK, Young R, Paul DS, et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat Genet. 2017;49(7):1113–9. https://doi.org/10.1038/ng.3874.CrossRefPubMedPubMedCentral

Harshfield EL, Koulman A, Ziemek D, Marney L, Fauman EB, Paul DS, et al. An unbiased lipid phenotyping approach to study the genetic determinants of lipids and their association with coronary heart disease risk factors. J Proteome Res. 2019;18(6):2397–410. https://doi.org/10.1021/acs.jproteome.8b00786.CrossRefPubMedPubMedCentral

1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. doi:https://doi.org/10.1038/nature11632.

Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e1000529. https://doi.org/10.1371/journal.pgen.1000529.CrossRefPubMedPubMedCentral

10.

Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167(5):1415–29. https://doi.org/10.1016/j.cell.2016.10.042.CrossRefPubMedPubMedCentral

11.

Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11(7):499–511. https://doi.org/10.1038/nrg2796.CrossRefPubMed

12.

Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. https://doi.org/10.1093/bioinformatics/btq340.CrossRefPubMedPubMedCentral

13.

Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47(3):284–90. https://doi.org/10.1038/ng.3190.CrossRefPubMedPubMedCentral

14.

Stacey D, Fauman EB, Ziemek D, Sun BB, Harshfield EL, Wood AM, et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 2019;47(1):e3. https://doi.org/10.1093/nar/gky837.CrossRefPubMed

15.

Opgen-Rhein R, Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol. 2007;1(1):37. https://doi.org/10.1186/1752-0509-1-37.CrossRefPubMedPubMedCentral

16.

Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol. 2011;5(1):21. https://doi.org/10.1186/1752-0509-5-21.CrossRefPubMedPubMedCentral

17.

Gobbi A, Iorio F, Dawson KJ, Wedge DC, Tamborero D, Alexandrov LB, et al. Fast randomization of large genomic datasets while preserving alteration counts. Bioinformatics. 2014;30(17):i617–23. https://doi.org/10.1093/bioinformatics/btu474.CrossRefPubMedPubMedCentral

18.

Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2(10):2366–82. https://doi.org/10.1038/nprot.2007.324.CrossRefPubMedPubMedCentral

19.

Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50. https://doi.org/10.1038/ng.2982.CrossRefPubMedPubMedCentral

20.

Chasman DI, Pare G, Mora S, Hopewell JC, Peloso G, Clarke R, et al. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 2009;5(11):e1000730. https://doi.org/10.1371/journal.pgen.1000730.CrossRefPubMedPubMedCentral

21.

Rueedi R, Ledda M, Nicholls AW, Salek RM, Marques-Vidal P, Morya E, et al. Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS Genet. 2014;10(2):e1004132. https://doi.org/10.1371/journal.pgen.1004132.CrossRefPubMedPubMedCentral

22.

Teslovich TM, Kim DS, Yin X, Stančáková A, Jackson AU, Wielscher M, et al. Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study. Hum Mol Genet. 2018;27(9):1664–74. https://doi.org/10.1093/hmg/ddy067.CrossRefPubMedPubMedCentral

23.

Demirkan A, van Duijn CM, Ugocsai P, Isaacs A, Pramstaller PP, Liebisch G, et al. Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genet. 2012;8(2):e1002490. https://doi.org/10.1371/journal.pgen.1002490.CrossRefPubMedPubMedCentral

24.

Tabassum R, Rämö JT, Ripatti P, Koskela JT, Kurki M, Karjalainen J, et al. Genetic architecture of human plasma lipidome and its link to cardiovascular disease. Nat Commun. 2019;10(1):4329. https://doi.org/10.1038/s41467-019-11954-8.CrossRefPubMedPubMedCentral

25.

Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–9. https://doi.org/10.1038/s41586-018-0175-2.CrossRefPubMedPubMedCentral

26.

Schaap FG, Rensen PC, Voshol PJ, Vrins C, van der Vliet HN, Chamuleau RA, et al. ApoAV reduces plasma triglycerides by inhibiting very low density lipoprotein-triglyceride (VLDL-TG) production and stimulating lipoprotein lipase-mediated VLDL-TG hydrolysis. J Biol Chem. 2004;279(27):27941–7. https://doi.org/10.1074/jbc.M403240200.CrossRefPubMed

27.

Ariza MJ, Sanchez-Chaparro MA, Baron FJ, Hornos AM, Calvo-Bonacho E, Rioja J, et al. Additive effects of LPL, APOA5 and APOE variant combinations on triglyceride levels and hypertriglyceridemia: results of the ICARIA genetic sub-study. BMC Med Genet. 2010;11(1):66. https://doi.org/10.1186/1471-2350-11-66.CrossRefPubMedPubMedCentral

28.

Johansen CT, Wang J, Lanktree MB, Cao H, McIntyre AD, Ban MR, et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat Genet. 2010;42(8):684–7. https://doi.org/10.1038/ng.628.CrossRefPubMedPubMedCentral

29.

Weissglas-Volkov D, Aguilar-Salinas CA, Nikkola E, Deere KA, Cruz-Bautista I, Arellano-Campos O, et al. Genomic study in Mexicans identifies a new locus for triglycerides and refines European lipid loci. J Med Genet. 2013;50(5):298–308. https://doi.org/10.1136/jmedgenet-2012-101461.CrossRefPubMed

30.

De Castro-Orós I, Cenarro A, Tejedor MT, Baila-Rueda L, Mateo-Gallego R, Lamiquiz-Moneo I, et al. Common genetic variants contribute to primary hypertriglyceridemia without differences between familial combined hyperlipidemia and isolated hypertriglyceridemia. Circ Cardiovasc Genet. 2014;7(6):814–21. https://doi.org/10.1161/CIRCGENETICS.114.000522.CrossRefPubMed

31.

Eiden M, Koulman A, Hatunic M, West JA, Murfitt S, Osei M, et al. Mechanistic insights revealed by lipid profiling in monogenic insulin resistance syndromes. Genome Med. 2015;7(1):63. https://doi.org/10.1186/s13073-015-0179-6.CrossRefPubMedPubMedCentral

32.

Sanders FWB, Acharjee A, Walker C, Marney L, Roberts LD, Imamura F, et al. Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption. Genome Biol. 2018;19(1):79. https://doi.org/10.1186/s13059-018-1439-8.CrossRefPubMedPubMedCentral

33.

Tang CS, Zhang H, Cheung CY, Xu M, Ho JC, Zhou W, et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat Commun. 2015;6(1):10206. https://doi.org/10.1038/ncomms10206.CrossRefPubMed

34.

Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83. doi:https://doi.org/10.1038/ng.2797.

35.

Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50(11):1514–23. https://doi.org/10.1038/s41588-018-0222-9.CrossRefPubMedPubMedCentral

36.

Wang WJ, Baez JM, Maurer R, Dansky HM, Cohen DE. Homozygous disruption of Pctp modulates atherosclerosis in apolipoprotein E-deficient mice. J Lipid Res. 2006;47(11):2400–7. https://doi.org/10.1194/jlr.M600277-JLR200.CrossRefPubMed

37.

Piperi C, Adamopoulos C, Papavassiliou AG. XBP1: a pivotal transcriptional regulator of glucose and lipid metabolism. Trends Endocrinol Metab. 2016;27(3):119–22. https://doi.org/10.1016/j.tem.2016.01.001.CrossRefPubMed

38.

Glimcher LH, Lee AH. From sugar to fat: how the transcription factor XBP1 regulates hepatic lipogenesis. Ann N Y Acad Sci. 2009;1173(Suppl):E2–9. https://doi.org/10.1111/j.1749-6632.2009.04956.x.CrossRefPubMedPubMedCentral

39.

Brandt C, Nolte H, Henschke S, Engström Ruud L, Awazawa M, Morgan DA, et al. Food perception primes hepatic ER homeostasis via melanocortin-dependent control of mTOR activation. Cell. 2018;175:1321-1335.e20. doi:https://doi.org/10.1016/J.CELL.2018.10.015.

40.

Macaluso FS, Maida M, Petta S. Genetic background in nonalcoholic fatty liver disease: a comprehensive review. World J Gastroenterol. 2015;21(39):11088–111. https://doi.org/10.3748/wjg.v21.i39.11088.CrossRefPubMedPubMedCentral

41.

Simons N, Isaacs A, Koek GH, Kuc S, Schaper NC, Brouwers MC. PNPLA3, TM6SF2, and MBOAT7 genotypes and coronary artery disease. Gastroenterology. 2017;152(4):912–3. https://doi.org/10.1053/j.gastro.2016.12.020.CrossRefPubMed

42.

Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50(4):559–71. https://doi.org/10.1038/s41588-018-0084-1.CrossRefPubMedPubMedCentral

43.

Gijón MA, Riekhof WR, Zarini S, Murphy RC, Voelker DR. Lysophospholipid acyltransferases and arachidonate recycling in human neutrophils. J Biol Chem. 2008;283(44):30235–45. https://doi.org/10.1074/jbc.M806194200.CrossRefPubMedPubMedCentral

44.

Takemasu S, Ito M, Morioka S, Nigorikawa K, Kofuji S, Takasuga S, et al. Lysophosphatidylinositol-acyltransferase-1 is involved in cytosolic Ca2+ oscillations in macrophages. Genes to Cells. 2019;24(5):366–76. https://doi.org/10.1111/gtc.12681.CrossRefPubMed

45.

Draisma HH, Pool R, Kobl M, Jansen R, Petersen AK, Vaarhorst AA, et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun. 2015;6(1):7208. https://doi.org/10.1038/ncomms8208.CrossRefPubMed

46.

Wu JH, Lemaitre RN, Manichaikul A, Guan W, Tanaka T, Foy M, et al. Genome-wide association study identifies novel loci associated with concentrations of four plasma phospholipid fatty acids in the de novo lipogenesis pathway: results from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortiu. Circ Cardiovasc Genet. 2013;6(2):171–83. https://doi.org/10.1161/CIRCGENETICS.112.964619.CrossRefPubMedPubMedCentral

47.

Hu Y, Tanaka T, Zhu J, Guan W, Wu JHY, Psaty BM, et al. Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations. J Lipid Res. 2017;58(5):974–81. https://doi.org/10.1194/jlr.P071860.CrossRefPubMedPubMedCentral

48.

Burgess S, Harshfield E. Mendelian randomization to assess causal effects of blood lipids on coronary heart disease: lessons from the past and applications to the future. Curr Opin Endocrinol Diabetes Obes. 2016;23(2):124–30. https://doi.org/10.1097/MED.0000000000000230.CrossRefPubMedPubMedCentral

49.

Saleheen D, Natarajan P, Armean IM, Zhao W, Rasheed A, Khetarpal SA, et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature. 2017;544(7649):235–9. https://doi.org/10.1038/nature22034.CrossRefPubMedPubMedCentral

50.

Di Angelantonio E, Thompson S, Kaptoge S, Moore C, Walker M, Armitage J, et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet. 2017;390(10110):2360–71. https://doi.org/10.1016/S0140-6736(17)31928-1.CrossRefPubMedPubMedCentral

Title: Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci
Authors: Eric L. Harshfield
Eric B. Fauman
David Stacey
Dirk S. Paul
Daniel Ziemek
Rachel M. Y. Ong
John Danesh
Adam S. Butterworth
Asif Rasheed
Taniya Sattar
Zameer-ul-Asar
Imran Saleem
Zoubia Hina
Unzila Ishtiaq
Nadeem Qamar
Nadeem Hayat Mallick
Zia Yaqub
Tahir Saghir
Syed Nadeem Hasan Rizvi
Anis Memon
Mohammad Ishaq
Syed Zahed Rasheed
Fazal-ur-Rehman Memon
Anjum Jalal
Shahid Abbas
Philippe Frossard
Danish Saleheen
Angela M. Wood
Julian L. Griffin
Albert Koulman
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Medicine / Issue 1/2021
Electronic ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-021-02087-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

Potential impact of individual exposure histories to endemic human coronaviruses on age-dependent severity of COVID-19

Development of a model of medication review for use in clinical practice: Bristol medication review model

Paediatric rotavirus vaccination, coeliac disease and type 1 diabetes in children: a population-based cohort study

Association of multimorbidity and changes in health-related quality of life following myocardial infarction: a UK multicentre longitudinal patient-reported outcomes study